The ATLAS experiment has recently commissioned a new hardware component of its first-level trigger: the topological processor (L1Topo). This innovative system, using state-of-the-art FPGA processors, selects events by applying kinematic and topological requirements on candidate objects (energy clusters, jets, and muons) measured by calorimeters and muon sub-detectors. Since the first-level trigger is a synchronous pipelined system, such requirements are applied within a latency of 200ns. We will present the first results from data recorded using the L1Topo trigger; these demonstrate a significantly improved background event rejection, thus allowing for a rate reduction without efficiency loss. This improvement has been shown for several physics processes leading to low-pT leptons, including H->tau tau and J/Psi->mu mu. In addition, we will discuss the use of an accurate L1Topo simulation as a powerful tool to validate and optimize the performance of this new trigger system. To reach the required accuracy, the simulation must take into account the limited precision that can be achieved with kinematic calculations implemented in firmware.

This contribution reviews the novel LHC luminosity control software stack. All luminosity-related manipulations and scans in the LHC interaction points are managed by the LHC luminosity server, which enforces concurrency correctness and transactionality. Operational features include luminosity optimization scans to find the head-on position, luminosity levelling, and the execution of arbitrary scan patterns defined by the LHC experiments in a domain specific language. The LHC luminosity server also provides full built-in simulation capabilities for testing and development without affecting the real hardware. The performance of the software in 2016 and 2017 LHC operation is discussed and plans for further upgrades are presented.

Accelerator control software often has to handle multi-dimensional data of physical quantities when aggregating readings from multiple devices (e.g. the reading of an orbit in the LHC). When storing such data as nested hashtables or lists, the ability to do structural operations or calculations along an arbitrary dimensions is hampered. Tensorics is a Java library to provide a solution for these problems. A Tensor is a n-dimensional data structure, and both structural (e.g. extraction) and mathematical operations are possible along any dimension. Any Java class or interface can serve as a dimension, with coordinates being instances of a dimension class. This contribution will elaborate on the design and the functionality of the Tensorics library and highlight existing use cases in operational LHC control software, e.g. the LHC luminosity server or the LHC chromaticity correction application.